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Chapter 5

Chapter 5. 5.1 The Basics of Counting 5.2 The Pigeonhole Principle 5.3 Permutations and Combinations 5.4 Binomial Coefficients 5.5 Generalized Permutations and Combinations 5.6 Generating Permutations and Combinations. Basic Counting Principles. Product rule Sum rule.

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Chapter 5

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  1. Chapter 5 • 5.1 The Basics of Counting • 5.2 The Pigeonhole Principle • 5.3 Permutations and Combinations • 5.4 Binomial Coefficients • 5.5 Generalized Permutations and Combinations • 5.6 Generating Permutations and Combinations

  2. Basic Counting Principles • Product rule • Sum rule

  3. Basic Counting Principles • THE SUM RULE: If task can be done either in one of n1 ways or in one of n2 ways, where none of the set of n1 ways is the same as any of the set of n2ways, then there are n1+n2ways to do the task. • Example: Suppose statement labels in a programming language must be a single letter or a single decimal digit. How many possible labels? HW: Example 13,(p.339)

  4. Basic Counting Principles • THE PRODUCT RULE: Suppose that a procedure can be broken down into a sequence of two tasks. If there are n1way to do the first task and for each of these ways of doing the first task, there are n2 ways to do the second task, then there are n1n2 ways to do the procedure. • Example: Statement labels in Basic can be either • a single letter or • a letter followed by a digit. How many possible labels? • HW: Example 3, (p.336)

  5. Basic Counting Principles • Example 7: Counting One-to-One functions : How many one-to-one functions are there from a set with m elements to one with n element? • Example 12: A student can choose a computer project from one of three lists. The three lists contain 23, 15, and 19 possible projects, respectively. No project is on more than one list. How many possible projects are there to choose from? • Example 9: what is the value of k after the following code has been executed? k := 0 for i1 := 1 to n1 for i2 := 1 to n2 ： for im := 1 to nm k := k+1

  6. More Complex Counting Problems • Example 15: Each user on a computer system has a password, which is six to eight characters long, where each character is an uppercase letter or a digit. Each password must contain at least one digit. How many possible passwords are there?

  7. More Complex Counting Problems • Counting Integer Addresses In the internet, which is made up of interconnected physical networks of computers, each computer is assigned an Internet address. In Version 4 of the Internet Protocol( IPv4) , now in use, an address is string of 32bits. It beings with network number (netid). The netidis followed by a host number (hostid) , which identifies a computer as a member of a particular network.

  8. More Complex Counting Problems • Three forms of addresses are used, with different numbers of bits used for netid and hostid. • Class A addresses, used for the largest networks, consist of 0, followed by a 7-bit netid and a 24-bit hostid. • ClassB addresses, used for medium-sized networks, consist of 10, followed by a 14-bit netid and a 16-bit hostid. • Class C addresses, used for the smallest networks, consist of 110, followed by a 21-bit netid and an 8-bit hostid.

  9. More Complex Counting Problems • There are several restrictions on addresses because of special uses: 1111111 is not available as the netid of a Class A network, and the hostids consisting of all 0s and all 1s are not available for use in any network. • How many different IPv4 addresses are available for computers on the internet?

  10. The Principle of Inclusion Exclusion • If A and B are not disjoint: |AB|=|A|+|B|-|AB| • Don't count objects in the intersection of two sets more than once! • Example : Find the number of three-letter initials where none of the letters is repeated. • Example: Count the number of bit strings of length 4 which begin with a 1 or end with a 00. • Example : Count the number of bit strings of length 4. (Apply the rule of product.) • Example : Count the number of bit strings of length 4 or less. (Apply the rule of sum) • Example : Count the set S of 3 digit numbers which begin or end with an even digit. • HW: example 17,(p.342)

  11. Tree Diagrams • Counting problems can be solved using tree diagrams. • Example 19: How many bit strings of length four do not have two consecutive 1s? FIGURE 2 Bit Strings of Length Four without Consecutive 1s.

  12. Tree Diagrams • Example 20: A playoff between two teams consists of at most five games. The first team that wins three games wins the playoff . In how many different ways can the playoff occur? FIGURE 3 Best Three Games Out of Five Playoffs.

  13. 5.2 The Pigeonhole Principle • Theorem 1: The Pigeonhole Principle If k is a positive integer and k+1 or more objects are placed into k boxes, then there is at least one box containing two or more of the objects. • Proof: We will prove the pigeonhole principle using a proof by contraposition. Suppose that none of the k boxes contains more than one object. Then the total number of objects would be at most k. this is a contradiction, because there are at least k+1 objects. • Corollary 1: A function f from a set with k+1 or more elements to a set with k elements is not one-to-one. • Example 1: Among any group of 367 people, there must be at least two with the same birthday, because there are only 366 possible birthdays. • HW: Example 3,(p.348)

  14. The Generalized Pigeonhole Principle • Theorem 2: The Generalized Pigeonhole Principle If N objects are placed into k boxes, then there is at least one box containing at least N/k objects. • Proof: We will use a proof by contradiction. Suppose that none of the boxes contains more than N/k -1 objects. Then , the total number of objects is at most k( N/k -1 ) < k( ( N/k +1 )-1)=N where the inequality N/k < (N/k) +1 has been used. This is a contradiction because there are a total of N objects.

  15. The Generalized Pigeonhole Principle • Example 5 : Among 100 people there are at least 100/12 =19 who were born in the same month. • Example 7: a) How many cards must be selected from a standard deck of 52 cards to guarantee that at least three cards of the same suit are chosen? b) How many must be selected to guarantee that at least three hearts are selected ?

  16. The Generalized Pigeonhole Principle • Example 9: Suppose that a computer science laboratory has 15 workstations and 10 servers. • A cable can be used to directly connect a workstation to a server. For each server, only one direct connection to that server can be active at any time. We want to guarantee that at any time any set of 10 or fewer workstations can simultaneously access different servers via direct connections. • Although we could do this by connecting every workstation directly to every server ( using 150 connections) . • what is the minimum number of direct connections needed to achieve this goal?

  17. Some Elegant Applications of the Pigeonhole Principle • Example 10: During a month with 30 days, a baseball team plays at least one game a day, but no more than 45 games. Show that there must be a period of some number of consecutive days during which the team must play exactly 14 games.

  18. 5.3 Permutations and Combinations Urn models • We are given set of n objects in an urn (don’t ask why it’s called an “urn” - probably due to some statistician years ago) . • We are going to pick (select) r objects from the urn in sequence. After we choose an object -- we can replace it-(selection with replacement) -- or not -(selection without replacement). • If we choose r objects, how many different possible sequences of r objects are there? • Does the order of the objects matter or not?

  19. Permutations • Selection without replacement of r objects from the urn with n objects. • Apermutation is an arrangement. Order matters . • The number of permutations of n things taken r at a time P(n,r) = n(n - 1)(n - 2) . . . (n - r + 1) • Note: P(n, r) = n! /(n - r)! • Example: Let A and B be finite sets and let | A |£| B | . Count the number of injections from A to B.

  20. Permutations • Theorem 1: If n is a positive integer and r is an integer with 1  r  n, then there are P(n , r) = n(n-1)(n-2) ‥ (n-r+1) r- permutations of a set with n distinct elements. • Corollary 1: If n and r are integers with 0  r  n , then P(n, r)= n! / (n-r)!

  21. Permutations • A permutation of a set of distinct objects is an ordered arrangement of these objects. • We also are interested in ordered arrangements of some of the elements of a set. • An ordered arrangement of r elements of a set is called an r-permutation. • Example 2: let S={1, 2, 3}. The ordered arrangement 3, 1, 2 is a permutation of S. The ordered arrangement 3, 2 is a 2-permutation of S. • Example 4: How many ways are there to select a first-prize winner, a second-prize winner, and a third-prize winner from 100 different people who have entered a contest? • HW: example 7,(p.357)

  22. Combinations • Selection is without replacement but order does not matter • It is equivalent to selecting subsets of size r from a set of size n. • Divide out the number of arrangements or permutations of r objects from the set of permutations of n objects taken r at a time: • The number of combinations of n things taken r at a time Other names for C(n, r): • n choose r • The binomial coefficient

  23. Combinations • Theorem 2: The number of r-combinations of a set with n elements, where n is a nonnegative integer and r is an integer with 0  r  n, equals C(n, r) = n! / r! (n-r)! • Corollary 2: Let n and r be nonnegative integers with r  n. Then C(n, r) = C(n, n-r).

  24. Combinations • Example: How many subsets of size r can be constructed from a set of n objects? • Corollary: r=0n C(n, r)=2n • Proof: • If we count the number of subsets of a set of size n, we get the cardinality of the power set.

  25. Combinations • Example 11: How many poker hands of five cards can be dealt from a standard deck of 52 card? Also, how many ways are there to select 47 cards from a standard deck of 52 cards? • Example 13: A group of 30 people have been trained as astronauts to go on the first mission to Mars. How many ways are there to select a crew of six people to go on this mission ( assuming that all crew members have the same job)? • Example 14: How many bit strings of length n contain exactly r 1s? • HW: Example 15, p(360)

  26. 5.4 Binomial Coefficients • Theorem 1: The binomial theorem Let x and y be variables, and let n be a nonnegative integer. Then • Example 3: What is the coefficient of x12y13 in the expansion of (x + y)25?

  27. The Binomial Theorem • Corollary 1: Let n be a nonnegative integer. Then • Corollary 2: Let n be a positive integer. Then • Corollary 3: Let n be a nonnegative integer. Then

  28. Pascal’s Identity and Triangle • Theorem 2: Pascal’s IdentityLet n and k be positive integers with n  k . Then

  29. Pascal’s Identity and Triangle FIGURE 1 Pascal’s Triangle.

  30. Some Other Identities of the Binomial Coefficients • Theorem 3: Vandermonde’s Identity Let m, n, and r be nonnegative integers with r not exceeding either m or n. Then • Corollary 4: If n is nonnegative integer, then • Theorem 4: Let n and r be nonnegative integers with r  n. Then

  31. 5.5 Generalized Permutations and Combinations Permutations with Repetition • Theorem 1: The number of r-permutations of a set of n objects with repetition allowed is nr. • Example 1: How many strings of length r can be formed from the English alphabet?

  32. Combinations with Repetition • Theorem 2: There are C(n+r-1, r) = C(n+r-1, n-1) r-combinations from a set with n elements when repetition of elements is allowed. • Example 4: Suppose that a cookie shop has four different kinds of cookies. How many different ways can six cookies be chosen? Assume that only the type of cookie, and not the individual cookies or the order in which they are chosen, matters. • Example 5: How many solutions does the equation x1 + x2 + x3 = 11 have, where x1, x2, and x3 are nonnegative integers? • HW: Example 3,(p.372)

  33. Combinations with Repetition • Example 6: What is the value of k after the following pseudocode has been executed? k :=0 for i1 := 1 to n for i2 := 1 to i1 : for im := 1 to im-1 k :=k+1

  34. Combinations with Repetition

  35. Permutations with Indistinguishable Objects • Some elements may be indistinguishable in counting problems. When this is the case , care must be taken to avoid counting things more than once. Consider example 7. • Example 7: How many different strings can be made by reordering the letters of the word SUCCESS?

  36. Permutations with Indistinguishable Objects • Theorem 3: The number of different permutations of n objects, where there are n1indistinguishable objects of type 1, n2 indistinguishable objects of type 2,. . . , and nk indistinguishable objects of type k, is n! /(n1! n2! ‥nk!)

  37. Distributing Objects into Boxes • Distinguishable Objects and Distinguishable Boxes • We first consider the case when distinguishable objects are placed into distinguishable boxes. Consider example 8 in which the objects are cards and the boxes are hands of players. • Theorem 4: The number of ways to distribute n distinguishable objects into k distinguishable boxes so that ni objects are placed into box i, i=1, 2, . . .,k, equals n! /(n1! n2!‥nk!) • Example 8: How many ways are there to distribute hands of 5 cards to each of four players from the standard deck of 52 cards?

  38. Distributing Objects into Boxes • Indistinguishable Objects and Distinguishable Boxes • example 9: How many ways are there to place 10 indistinguishable balls into eight distinguishable bins?

  39. Distributing Objects into Boxes • Distinguishable Objects and Indistinguishable Boxes • Example 10: How many ways are there to put four different employees into three indistinguishable offices, when each office can contain any number of employees?

  40. Distributing Objects into Boxes • Indistinguishable Objects and Indistinguishable Boxes • Example 11: How many ways are there to pack six copies of the same book into four identical boxes, where a box can contain as many as six books?

  41. 5.6 Generating Permutations and Combinations Generating Permutations • Many different algorithms have been developed to generate the n! permutations of this set. • We will describe one of these that is based on the lexicographic (or dictionary ) ordering of the set of permutations of {1, 2, 3, . . ., n}. In this ordering, the permutation a1a2. . . anprecedes the permutation of b1b2. . .bn, if for some k, with 1 k  n , a1=b1 , a2=b2 , . . .,ak-1=bk-1, and ak< bk. • Example 1: The permutation 23415 of the set {1, 2, 3, 4, 5} precedes the permutation 23514. The permutation 41532 precedes 52143.

  42. Generating Permutations • A general method can be described for producing the next larger permutation in increasing order following a given a1a2. . . an . • First , find the integers ajand aj+1 with aj < aj+1and aj+1 > aj+2 > ‥ > an , that is, the last pair of adjacent integers in the permutation where the first integer in the pair is smaller than the second. Then, the next larger permutation in lexicographic order is obtained by putting in the jth position the least integer among aj+1 ,aj+2 , . . and an that is greater thanajand listing in increasing order the rest of the integers aj ,aj+1 , . . , anin positions j+1 to n. Example 2: What is the next permutation in lexicographic order after 362541? • Example 3: Generate the permutations of the integers 1, 2, 3 in lexicographic order?

  43. Generating Permutations Algorithm 1 :Generating the Next Permutation in Lexicographic Order. Procedure next permutation ( a1a2. . .an : permutation of {1, 2, . . .,n} j :=n-1 not equal to n, n-1,. . .,2, 1) whileaj> aj+1 j :=j-1 {j is the largest subscript with aj< aj+1} k :=n While aj> ak k :=k-1 {akis the smallest integer greater than j to the right of aj} interchange ajand akr :=n s :=j+1 while r > s begin interchange arand asr := r-1 s := s+1 End { this puts the tail end of the permutation after the jth position in increasing order}

  44. Generating Combinations • How can we generate all the combinations of the elements of a finite set? Because a combination is just a subset, we can use the correspondence between subsets of {a1,a2, . . .,an} and bit strings of length n. • Recall that a bit string corresponding to a subset has a 1 in position k if ak is in the subset, and has a 0 in this position if akis not in the subset. If all the bit strings of length n can be listed, then by the correspondence between subsets and bit strings, a list of all the subsets is obtained. • Example 4: Find the next bit string after 10 0010 0111.

  45. Generating Combinations • Algorithm 2: Generating the Next Larger Bit String procedure next bit string (bn-1 bn-2. . .b1b0: bit string not equal to 11. . .11) i:=0 whilebi:=1 begin bi :=0 i:= i+1 end bi:=1

  46. Generating Combinations • Example 5: Find the next larger 4-combination of the set {1, 2, 3, 4, 5, 6} after {1, 2, 5, 6} • Algorithm 3: Generating the Next r-Combination in Lexicographic Order. procedurenext r-combination ({a1,a2,. . .,ar}: proper subset of {1, 2,. . .,n} not equal to {n-r+1,. . .,n} with a1< a2<. . .< ar) i := r whileai=n-r+i i:= i-1 ai:= ai+1 forj :=i+1 tor aj :=ai +j -i

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